The traditional educational process of blind people is a complex practice that relies on the haptic perception (tactile perception) of physical models. However, physical models may be costly, inaccessible or may require a large storage space. To overcome these difficulties, in this paper a virtual haptic perception approach to support the teaching and learning process of blind people is proposed. The proposed approach combines the use of virtual reality and haptic technologies. The research aim is to objectively evaluate the feasibility and effectiveness of using virtual haptic perception in the education of blind children. For this purpose, an experimental methodology was defined and used to teach maths, in particular fundamental 3D shapes, to blind children. The results are analysed in terms of the participants' ability to explore and recognize virtual objects, and the knowledge gain after the virtual perception learning period. From this analysis it is concluded that haptic virtual perception is a valid and effective assistive technology for the education of blind children.Recently, in the attempt to increase blind people autonomy and improve their quality of life, a lot of effort has been devoted to develop technological travel aids. These systems can surrogate spatial information about the environment and deliver it to end-users through sensory substitution (auditory, haptic). However, despite the promising research outcomes, these solutions have met scarce acceptance in real-world. Often, this is also due to the limited involvement of real end users in the conceptual and design phases. In this manuscript, we propose a novel indoor navigation system based on wearable haptic technologies. All the developmental phases were driven by continuous feedback from visually impaired persons. https://www.selleckchem.com/products/sr10221.html The proposed travel aid system consists of a RGB-D camera, a processing unit to compute visual information for obstacle avoidance, and a wearable device, which can provide normal and tangential force cues for guidance in an unknown indoor environment. Experiments with blindfolded subjects and visually impaired participants show that our system could be an effective support during indoor navigation, and a viable tool for training blind people to the usage of travel aids.Recent years have witnessed widespread adoption of machine learning (ML)/deep learning (DL) techniques due to their superior performance for a variety of healthcare applications ranging from the prediction of cardiac arrest from one-dimensional heart signals to computer-aided diagnosis (CADx) using multi-dimensional medical images. Notwithstanding the impressive performance of ML/DL, there are still lingering doubts regarding the robustness of ML/DL in healthcare settings (which is traditionally considered quite challenging due to the myriad security and privacy issues involved), especially in light of recent results that have shown that ML/DL are vulnerable to adversarial attacks. In this paper, we present an overview of various application areas in healthcare that leverage such techniques from security and privacy point of view and present associated challenges. In addition, we present potential methods to ensure secure and privacy-preserving ML for healthcare applications. Finally, we provide insight into the current research challenges and promising directions for future research.Blood flow in the human vascular system is a complex to understand example of fluid dynamics in a closed conduit. Any irregularities in the hemodynamics may lead to lethal cardiovascular disease like heart attack, heart failure and ischemia. Numerical simulation of hemodynamics in the blood vessel can facilitate a thorough understanding of blood flow and its interaction with the adjacent vessel wall. A good simulation approach for blood flow can be helpful in early prediction and diagnosis of the mentioned disease. The simulation outcomes may also provide decision support for surgical planning and medical implants. This study reports an extensive review of various approaches adopted to analyze the influence of blood rheological characteristics in a different class of blood vessels. In particular, emphasis was given on the identification of best possible rheological model to effectively solve the hemodynamics inside different blood vessels. The performance capability of different rheological models was discussed for different classes and conditions of vessels and the best/poor performing models are listed out. The Carreau, Casson and generalized power-law models were appeared to be superior for solving the blood flow at all shear rates. In contrast, power law, Walburn-Scheck and Herchel-Bulkley model lacks behind in the purpose.With the advancement in artificial intelligence (AI) and machine learning (ML) techniques, researchers are striving towards employing these techniques for advancing clinical practice. One of the key objectives in healthcare is the early detection and prediction of disease to timely provide preventive interventions. This is especially the case for epilepsy, which is characterized by recurrent and unpredictable seizures. Patients can be relieved from the adverse consequences of epileptic seizures if it could somehow be predicted in advance. Despite decades of research, seizure prediction remains an unsolved problem. This is likely to remain at least partly because of the inadequate amount of data to resolve the problem. There have been exciting new developments in ML-based algorithms that have the potential to deliver a paradigm shift in the early and accurate prediction of epileptic seizures. Here we provide a comprehensive review of state-of-the-art ML techniques in early prediction of seizures using EEG signals. We will identify the gaps, challenges, and pitfalls in the current research and recommend future directions.Clinical decision-making in healthcare is already being influenced by predictions or recommendations made by data-driven machines. Numerous machine learning applications have appeared in the latest clinical literature, especially for outcome prediction models, with outcomes ranging from mortality and cardiac arrest to acute kidney injury and arrhythmia. In this review article, we summarize the state-of-the-art in related works covering data processing, inference, and model evaluation, in the context of outcome prediction models developed using data extracted from electronic health records. We also discuss limitations of prominent modeling assumptions and highlight opportunities for future research.